EE656A: ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, DEEP LEARNING & ITS APPLICATIONS (2022-23 Sem II) |
This course focuses on the theoretical advancements in the field of Artificial Intelligence, Machine Learning, Deep Learning, and their real-life applications. It is best suited for the PG students of all departments and interdisciplinary programs.
This course will provide the basic background as well as recent developments in the field of Artificial Intelligence (AI), Machine Learning, and Deep Learning. Illustration of different problems related to these fields will be discussed in the course along with their applications into different real-life problems but not limited to like signal processing, computer vision, intelligent control, transportation, prognosis and health management, bioinformatics, etc.
Artificial Intelligence (AI): Introduction, History, and Evolution
Agents of Artificial Intelligence
Introduction to Fuzzy System (FS), Artificial Neural Network (ANN), Evolutionary Computing (EC), Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Algorithm (PSO), etc.
Machine Learning: Unsupervised Learning, Supervised Learning, Semi supervised Learning, Reinforcement Learning
Clustering and Biclustering: K-means, Fuzzy c-means (FCM), Self-organizing maps (SOM), and other Clustering Algorithms
Classification: Support Vector Machines (SVM), K Nearest Neighbour (KNN), ANN, Fuzzy Rule Based, and other Classifiers
Curve fitting, Regression models, Prediction/Forecasting: ANN and Fuzzy Rule Based Regression Models
Performance Measures for Clustering, Biclustering, Classification, and Regression Algorithms
Deep Learning and Transfer Learning: Deep Neural Networks (DNN), Fuzzy Neural Networks (FNN), etc.
Case studies in the areas of signal processing, computer vision, intelligent control, transportation, prognosis and health management, bioinformatics, etc.
Prof. Nishchal K. Verma (nishchal@iitk.ac.in)
Mr. Arun Kumar Sharma (arnksh@iitk.ac.in) and Mr. Seetaram Maurya (seetaram@iitk.ac.in)
Lecture Schedule: Tuesday and Wednesday (12:00 to 13:15)
Lecture Venue: L-11
Lab schedule: Tuesday (14:00 to 17:00)
Lab Venue: ACES 107